1. Continuous Deployment of AI Models with Kubernetes & ArgoCD


    Continuous deployment of AI models with Kubernetes and ArgoCD is an advanced topic, but I'll walk you through the process of setting up a basic infrastructure to accomplish this. We leverage Kubernetes for orchestration of containerized applications, and ArgoCD for continuous deployment by syncing our application state with a desired state defined in a Git repository.

    We need to create a Kubernetes cluster where our AI models will run. For the purpose of this explanation, we will be using the Azure Kubernetes Service (AKS), although you can use any Kubernetes provider of your choice. Within this cluster, we deploy ArgoCD to manage the lifecycle of our applications automatically.

    With ArgoCD, changes to our applications, such as updated AI models or configurations, can be made in a Git repository. ArgoCD will detect these changes and apply them to our Kubernetes cluster, ensuring that the deployed applications match the state we've defined.

    Here's a breakdown of the steps we are going to follow in the Pulumi program:

    1. Create a Kubernetes cluster on Azure using azure_native.containerservice.ManagedCluster.
    2. Install ArgoCD on the Kubernetes cluster.
      • For the purpose of this explanation, we'll assume you've already got ArgoCD's manifest files in a Git repository.
      • We will simulate the installation steps, but in a real scenario, you can have a script or use a Pulumi component that installs ArgoCD on your cluster.
    3. Define an ArgoCD Application resource with the details of the Git repository containing your Kubernetes manifests for the AI model.

    Let's write a Pulumi program in Python to create an AKS cluster and set up ArgoCD for continuous deployment.

    import pulumi import pulumi_azure_native as azure_native from pulumi_azure_native import containerservice, resources # Step 1: Create a resource group resource_group = resources.ResourceGroup('rg') # Step 2: Create the AKS cluster where AI models will be deployed aks_cluster = containerservice.ManagedCluster( "aksCluster", resource_group_name=resource_group.name, agent_pool_profiles=[{ "count": 3, "max_pods": 110, "mode": "System", "name": "agentpool", "node_labels": {}, "os_disk_size_gb": 30, "os_type": "Linux", "vm_size": "Standard_DS2_v2", }], dns_prefix=resource_group.name, enable_rbac=True, kubernetes_version="1.20.7", linux_profile={ "admin_username": "azureuser", "ssh": { "publicKeys": [{ "keyData": "ssh-rsa ..." }] } }, node_resource_group=f"{resource_group.name}-aks", service_principal_profile={ "clientId": "your-service-principal-client-id", "secret": "your-service-principal-client-secret", } ) # Step 3: Get the kubeconfig from the AKS cluster (mocked here for brevity) kubeconfig = pulumi.Output.secret("your-kubeconfig") # Step 4: Set up ArgoCD Application # [Assuming argocd_application.py is a script that installs ArgoCD Application # using the supplied kubeconfig. This typically involves using tools like kubectl # which we will mock here for simplicity.] # # import argocd_application # argocd_application.setup_argocd(kubeconfig) # Export the kubeconfig pulumi.export("kubeconfig", kubeconfig)

    This program does the following:

    • Defines an Azure resource group where all the resources will live.
    • Creates an AKS cluster with a Linux node pool for running our containers.
    • Mocks fetching the kubeconfig necessary for connecting to the cluster. Normally, you would extract this from the AKS resource.
    • Mocks the setup of an ArgoCD Application (since setting up ArgoCD properly with Pulumi involves multiple steps such as using kubectl to apply manifests which we don't cover here).

    Note on Security:

    • For the SSH public key, use your own public key for secure access to the nodes.
    • The service principal credentials are sensitive and I'm using placeholders. Replace "your-service-principal-client-id" and "your-service-principal-client-secret" with your actual Azure service principal credentials. These should be stored and handled securely (e.g., using Pulumi's secret management).

    After the cluster and ArgoCD are set up, you can define the deployment pipeline in the Git repository that ArgoCD will monitor.

    When you apply this Pulumi program, it will provision the necessary infrastructure on Azure. Your next steps would involve configuring ArgoCD to pull from your Git repository and setting up your application manifests within that repository. The manifests should define everything needed to run your AI models, including the container images, computing resources, and any service exposures or ingress routes necessary for consuming the model's predictions.